title: Novel likelihood ratio tests for screening gene-gene and gene-environment interactions with unbalanced repeated-measures data.
abstract: There has been extensive literature on modeling gene-gene interaction (GGI) and gene-environment interaction (GEI) in case-control studies with limited literature on statistical methods for GGI and GEI in longitudinal cohort studies. We borrow ideas from the classical two-way analysis of variance literature to address the issue of robust modeling of interactions in repeated-measures studies. While classical interaction models proposed by Tukey and Mandel have interaction structures as a function of main effects, a newer class of models, additive main effects and multiplicative interaction (AMMI) models, do not have similar restrictive assumptions on the interaction structure. AMMI entails a singular value decomposition of the cell residual matrix after fitting the additive main effects and has been shown to perform well across various interaction structures. We consider these models for testing GGI and GEI from two perspectives: likelihood ratio test based on cell means and a regression-based approach using individual observations. Simulation results indicate that both approaches for AMMI models lead to valid tests in terms of maintaining the type I error rate, with the regression approach having better power properties. The performance of these models was evaluated across different interaction structures and 12 common epistasis patterns. In summary, AMMI model is robust with respect to misspecified interaction structure and is a useful screening tool for interaction even in the absence of main effects. We use the proposed methods to examine the interplay between the hemochromatosis gene and cumulative lead exposure on pulse pressure in the Normative Aging Study.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA. yianko@umich.edu
authors.forename: Yi-An
authors.initials: YA
authors.lastname: Ko
authors.forename: Paramita
authors.initials: P
authors.lastname: Saha-Chaudhuri
authors.forename: Sung Kyun
authors.initials: SK
authors.lastname: Park
authors.forename: Pantel Steve
authors.initials: PS
authors.lastname: Vokonas
authors.forename: Bhramar
authors.initials: B
authors.lastname: Mukherjee
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000235:genetics; D003198:Computer Simulation; D004843:Epistasis, Genetic; D059647:Gene-Environment Interaction; D000071020:Hemochromatosis Protein; D015395:Histocompatibility Antigens Class I / Q000235:genetics; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008565:Membrane Proteins / Q000235:genetics; D008875:Middle Aged; D008957:Models, Genetic; D012044:Regression Analysis
chemical_list: C103673:HFE protein, human; D000071020:Hemochromatosis Protein; D015395:Histocompatibility Antigens Class I; D008565:Membrane Proteins
keywords: likelihood ratio test; longitudinal data; mixed model; nonadditivity; parametric bootstrap; singular value decomposition; two-step regression